7 Nov 2002 Niels Tuning - Vertex 2002 1
A vertex trigger for LHCb
The trigger for LHCb …
.. and the use of the Si vertex detector at the first and second trigger levels
Vertex2002 Hawai’i, 7 Nov 2002 Niels Tuning (CERN) (on behalf of LHCb)
7 Nov 2002 Niels Tuning - Vertex 2002 2
LHCbA Large Hadron Collider Beauty Experiment for Precision Measurements of CP-Violation and Rare Decays
Colliding beams: 25 ns 7 TeV x 7 TeV pp L = 2.1032 cm-2 s-1
(visible) = 68 mb (ppbbX) = 0.5 mb ~1012 bb / year BR(interesting channels) ~10-2 –
10-9
Finding B-mesons: High PT decay products Large lifetime sec.vertex Invariant mass
A low multiplicity B+- event
LHCb trigger =looking for a needle in a haystack… …every 25 ns!
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LHCb Trigger – Overview
L0: high PT
Pile-up Veto, using VETO detector
High ET calorimeter objects High PT muons
L1: high PT + impact parameter High impact parameter
tracks, using VELO detector
High PT tracks, using TT detector and L0 info
L2+L3: high PT + displaced vertex + B-mass + PID
Use tracking stations and RICH
Bunch crossing rate 40 Mhz
Non empty bc rate 30 MHz
Visible interaction rate ~12 MHz
Input to L0 ~10 MHz
Input to L1 1 MHz
Input to higher level
40 kHz
Writing to disk 0.2 kHz
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LHCb detector
L0 veto L1 L0 trigger
TT
T1 T2 T3
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L0 – Pile-up VETO(L0 = first trigger level)
Purpose: remove multiple interactions
Nominal luminosity: L = 2.1032 cm-2 s-1
Single : Double : Triple 16 : 4 : 1 75% : 20% : 5%
Why? More difficult to find high IP tracks at L1 Reduce bandwidth for L0
Detector: 2 Si disks (4 sensors) Same sensors as VErtex LOcator
(see talk J.Palacios) Only R information Use Beetle chip
OR of 4 strips: comparator output of 4 channels
1280 channels for 2 disks
320 strips
84
mm
16
mm
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L0 – Pile-up VETO algorithm
Calculate vertex for all combinations of 2 points a and b.Find highest peak (PV)Remove the hits and find 2nd peakVeto if peak>threshold(Zvtx) 2.8 mm, (beam) 53 mm
vtxa
vtxb
a
b
ZZ
ZZ
R
R
Z vtx (cm)
Ra (cm)
Rb
(cm
)com
bin
ati
on
s
true all
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L0 – Pile-up VETO performance
B+- L0 efficiency increase from 50% to 60% the L0 PT,HADR threshold can be
lowered from 4 GeV to 3.6 GeV
Reduce bandwidth and enhance purity:
Pileup VETO vetoes ~15% of all events Vetoed events are more likely to trigger
Only small inefficiency for single interactions: ~5%Reject ~30% of multiple interactions (NB: multiple interactions include inelastic+elastic !)
2events singlefor accept L0events for vetoedaccept L0
Same L0 output rate!
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CPU
SCI
Scheduling network
RU
L1 trigger: vertex trigger(L1 = second trigger level)
Implementation: Clustering in FPGA on front-end
Send data to RU (3-4 GB/s) CPU-farm:
300 – 400 CPUs 2D torus
Use scheduler Prototype with 32 CPUs running at 1.24 MHz
Buffer depth: 1820 events Latency = 1.65 msStrategy:
Find 2d-tracks with R-sensors and reconstruct vertex Reconstruct high-impact parameter tracks in 3d Extrapolate to TT through small magnetic field PT
Match track to L0 Muon objects PT and PID Select B–events using impact parameter and PT
information
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L1: VErtex LOcator(see talk by Juan Palacios)
Si: 220 m thick, n-on-n, Pitch: 37–98 m, R 40–92 m Sens. area: 0.8 cm < R < 4.2 cm
21 stations (84 sensors) -17.5 cm < Z < 75 cm 170,000 channels
RF foil: Very thin “beampipe” to separate prim. and sec. vacua
R sensor2048 strips
sensor: stereo angle +10o,-20o
R sensor: 4 inner and 2 outer sectors
sensor2048 strips
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L1: input data
Velo clusters: Clusters are found in FPGAs
per groups of 32 strips. Digital (offline is analog)
~1000 clusters ~0.1% noise clusters (200)
TT clusters ~300 clusters
L0 objects 3 muons Some calorimeter data
Cluster resolution:(testbeam)
=14 m
1000 clusters(simulation)
Pitch (m)
Reso
luti
on (m
)
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multiplicity
~60
L1: track reconstruction
Look only for R-clusters: 2d RZ-tracks Fast!
Find “triplets” of clustersCombine triplets~98% efficiency for B-tracks
Z vtx histogram X,Y vtx
VELO is being redesigned to 45o sectors:
faster L1 tracking
lower noise less 2d tracks
2d tracks in a 90o sector:
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L1: primary vertex
Primary vertex reconstructed with 2d tracksXY information from segmentationFlight direction of B is forward RZ projection of impact parameter contains most information
X Z
Vertex resolution:Impact parameter:
2d 3d
Lifetime
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L1: PT information – L0
Match VELO track to Muon from L0: PID Momentum
Efficient selection of BsJ/() BdJ/() Ks
Enhance -tagged sample
VELO
MAGNETMUON
dp/p=4.8%
TT
CAL
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Good momentum resolution, cut on J/ mass:
~60% of events contain both muons
<1% min.bias retention
OR require 1 muon with high PT, high IP
~75 % efficiency ~3% retention
Work ongoing: achieved 90% eff. using
neural net
L1: algorithm (1)Preliminary
J/ mass
Performance with all event info (except TT)
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L1: Trigger Tracker
TT = Tracking station before the magnet
Design still under studySi: 400 - 500 m thickWide pitch: 200 m
Sensor dimensions: 7.8 x 11.0 cm2
4 layers (x,u [30 cm gap] v,x) Stereo angle: 00, -50, +50, 00
To be optimized
836 sensors (~7 m2)
141 cm
11
6 c
m
VELO
TT M
AG
NE
T
RICH-1
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L1: PT information – TT
High impact parameter 2d tracks are reconstructed in 3d and extrapolated to TT1
Magnetic field between VELO and TT:
B dl 0.1 Tm
Ensures momentum information
dp/p ~ 30%
30% ---
p (GeV)
30
GeV
---
d
p/p
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L1: algorithm (2)Preliminary
Get two highest PT tracks, using TT
Consider impact parameter and PT of these tracks
Look in plane IP/(IP) vs PT
Bd+-BsDs
-K+
Signal
Minimum bias
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Tracking+Vertexing
time (ms)
Even
ts
----
17 m
s
L1: performance - timing
Remember: latency ~1.7 ms Possibly x32 more. A more flexible system is under
study were CPUs from DAQ can be used for the TRIGGER and vice versa
Tracking+Vertexing: < 20 ms2007 CPUs: x8 fasterOptimize algorithm+code in the right ballpark!
2d tracking: ~70%Vertex: ~15%3d tracking (a few) ~15%
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L1: performance
Efficiency vs retention: (Example B+- )
Expected overall trigger performance: (cumulative)
Bd+-
L1+L0 info
L1+PT info
40 kHz
L0L1 L1L2
L1 o
utp
ut
rate
(M
Hz)
Bd+- efficiency
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Conclusions
The pileup-VETO detector efficiently rejects multiple primary vertices @ 40 MHz at L0
The VELO detector reconstructs primary vertices at L1 with excellent resolution
(Zvtx) 60 m high impact parameter tracks can be identified
The TT detector - or L0 information - enables measuring the momentum of tracks
Efficient L1 selection algorithms under study Efficiency of 70% (90%) for B+- ( BJ/() Ks)
reachable at 4% minimum bias retention
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